Editorial
Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses
American journal of epidemiology, v 194(2), pp 322-326
10 Dec 2024
Abstract
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) presented a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high-dimensional data. The tools for implementing deep learning methods are not as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, health care providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiologic principles of assessing bias, study design, interpretation, and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.
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Details
- Title
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses
- Creators
- D. Alex Quistberg (Corresponding Author) - Drexel UniversityStephen J. Mooney - University of WashingtonTolga Tasdizen - University of UtahPablo Arbelaez - Universidad de Los AndesQuynh C. Nguyen - Universidad de Los Andes
- Publication Details
- American journal of epidemiology, v 194(2), pp 322-326
- Publisher
- Oxford Univ Press
- Number of pages
- 5
- Grant note
- K01TW011782 / Fogarty International Center of the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH Fogarty International Center (FIC) R01MD016037 / National Institute on Minority Health and Health Disparities; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Minority Health & Health Disparities (NIMHD) R00LM012868; R01LM012849 / National Library of Medicine; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM)
- Resource Type
- Editorial
- Language
- English
- Academic Unit
- Urban Health Collaborative; Environmental and Occupational Health
- Web of Science ID
- WOS:001373558300001
- Scopus ID
- 2-s2.0-85217794808
- Other Identifier
- 991022008196904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health